estimating the Hessian matrix \(\chi \)  numerically from the data. The matrix \(\chi \)  has units of bits, as we chose the thresholds to be dimensionless. The eigenvectors of \(\chi \)  determine the combinations of thresholds that have independent effects on the information, and the eigenvalues \(\lbrace \lambda _{\rm i}\rbrace \)  of \(\chi \)  (also in bits) determine the sensitivity along these independent directions. As the number of thresholds increases we find a wider spread of eigenvalues, as in a wide class of “sloppy models” [1]}, [2]}. This means that some combinations of thresholds are two orders of magnitude more important than others.
